Students tend to take far from optimal ways to assimilate information, especially when it comes to exams. Scientists Tim Novikoff, Jon Kleinbert, and Steve Strogatz decided to take a mathematical approach to the way that students learn, in order to find the most effective way to succeed.
The new study was published in PNAS, and they first started out by examining different theories. One of the first ones they considered was the spacing effect, which entails spreading out learning so that a student is more likely to learn it. Then they considered the theory of expanded retrieval, which means that the more you are exposed to a subject, the more you retain it, and reduced them as much as possible in order to model them.
The model becomes complex when a student tries to learn a number of facts, each with its own time constraints. There are limits to what students can learn, as demonstrated by a ‘finicky slow student’, who is obsessed with constant review at a very slow pace. This kind of student will never perfectly learn a given subject.
This kind of approach could be very useful in order to tailor education to individual students, who will all assimilate information in a different way, and at different paces. The algorithmic approach would allow educators to schedule it optimally, so that the students would learn better.
Reference: “Education of a model student” by Timothy P. Novikoff, Jon M. Kleinberg and Steven H. Strogatz, 23 January 2012, Proceedings of the National Academy of Sciences.